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Workshop
Gaze meets ML
Ismini Lourentzou · Joy T Wu · Satyananda Kashyap · Alexandros Karargyris · Leo Anthony Celi · Ban Kawas · Sachin S Talathi

@ Physical
Event URL: https://gaze-meets-ml.github.io/ »

Eye gaze has proven to be a cost-efficient way to collect large-scale physiological data that can reveal the underlying human attentional patterns in real-life workflows, and thus has long been explored as a signal to directly measure human-related cognition in various domains. Physiological data (including but not limited to eye gaze) offer new perception capabilities, which could be used in several ML domains, e.g., egocentric perception, embodied AI, NLP, etc. They can help infer human perception, intentions, beliefs, goals, and other cognition properties that are much needed for human-AI interactions and agent coordination. In addition, large collections of eye-tracking data have enabled data-driven modeling of human visual attention mechanisms, both for saliency or scanpath prediction, with twofold advantages: from the neuroscientific perspective to understand biological mechanisms better, and from the AI perspective to equip agents with the ability to mimic or predict human behavior and improve interpretability and interactions.

With the emergence of immersive technologies, now more than any time there is a need for experts of various backgrounds (e.g., machine learning, vision, and neuroscience communities) to share expertise and contribute to a deeper understanding of the intricacies of cost-efficient human supervision signals (e.g., eye-gaze) and their utilization towards by bridging human cognition and AI in machine learning research and development. The goal of this workshop is to bring together an active research community to collectively drive progress in defining and addressing core problems in gaze-assisted machine learning.

Author Information

Ismini Lourentzou (Virginia Tech)
Joy T Wu (Almaden Research Center, International Business Machines)
Satyananda Kashyap (IBM Research)

Satyananda Kashyap's research focuses on developing novel machine learning techniques to tackle problems in healthcare. Before joining IBM Research, he pursued his Ph.D. at the University of Iowa. At Iowa, his work focused on developing novel graph-based machine learning algorithms on longitudinal Knee MRIs to quantify and understand the degeneration of the knee joint with the progression of osteoarthritis. Currently, his research focuses on the problem of chest x-ray specifically on developing explainable AI methods for classifying the various diseases so that the machine diagnosis can be understood by a human and trusted.

Alexandros Karargyris (IHU Strasbourg)
Leo Anthony Celi (Massachusetts Institute of Technology)
Ban Kawas (Meta AI)
Sachin S Talathi (Qualcomm Inc)

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